One of the greatest challenges that Internet advertisers and publishers currently face is providing relevant content to their consumers across their touch points. The concept of “ad blindness” (e.g., a tendency for users to ignore anything that is separate from main content of a website) is a problem that dramatically reduces the value of an advertisement. Thus, advertising effectiveness can be improved by understanding the likes and dislikes of consumers.
One way to determine a customer's preferences is to collect information directly from the customer. Although effective, some of the problems with using specific information is that the data needs to be voluntarily provided, isn't always accurate, is typically tied to personally identifiable information, is based on the preferences of that particular day and is difficult to update.
Another mechanism for determining user preferences is to track the behavioral data of users as they view web pages, and purchase products in the internet. This implicit data collection can be accomplished anonymously or tied to a specific known identification. This sort of behavioral preference information can also be aggregated in relation to content items to benefit first-time visitors with the knowledge and experience of people who have come before them about what is most relevant.
Complimentary to recommendations and advertising directly on web pages is the e-mail marketing channel, which offers another touch-point with potential customers and loyal users. By understanding what is happening on a website (e.g., new content, changing meta-data and status, etc.) and each consumer's likes, either explicitly or implicitly, e-mail campaigns can be more effective.
Therefore what has been needed and heretofore unavailable is a system and method for determining in real time fresh information that fits user preferences and the current state of available offerings to enable e-mail marketing campaigns that are more relevant and interesting to engage and retain recipients.